Federated Deep Reinforcement Learning for Internet of Things with Decentralized Cooperative Edge Caching
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2020-10
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Language
en
Pages
15
9441-9455
9441-9455
Series
IEEE Internet of Things Journal, Volume 7, issue 10
Abstract
Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the expectation convergence of FADE. Trace-driven simulation results demonstrate the effectiveness of the proposed FADE framework on reducing the performance loss and average delay, offloading backhaul traffic, and improving the hit rate.Description
| openaire: EC/H2020/871780/EU//MonB5G
Keywords
Cooperative caching, deep reinforcement learning (DRL), edge caching, federated learning, hit rate, Internet of Things (IoT)
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Citation
Wang, X, Wang, C, Li, X, Leung, V C M & Taleb, T 2020, ' Federated Deep Reinforcement Learning for Internet of Things with Decentralized Cooperative Edge Caching ', IEEE Internet of Things Journal, vol. 7, no. 10, 9062302, pp. 9441-9455 . https://doi.org/10.1109/JIOT.2020.2986803